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Romanize Livonian to clean ASCII

livonian_romanize
Read-onlyIdempotent

Strip Livonian orthography down to clean, pronounceable ASCII for an English-trained downstream (a voice/TTS, a search box). See the text parameter doc for the exact letter mappings. Returns Markdown plus the romanized output as structuredContent matching the declared outputSchema. Pure local transform: no dictionary lookup, no network, and the output is always ASCII.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesLivonian text to reduce to clean, pronounceable ASCII. Strips macrons, diacritics and capitals (ā→aa, š→sh, õ→u), softens a palatalized consonant (ņ ļ ŗ ţ ḑ) only before a vowel (ņ+a→nya, else ikšiggiņ→ikshiggin), and hyphenates the broken tone (si'zzõl→siz-zul) and long-vowel diphthong glides (kīen→kee-en). Pass a word, phrase, or several lines.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesThe romanized ASCII output.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / title
      Previous value: -"romanize_livonianArguments"New value: +"livonian_romanizeArguments"
  2. First observed

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower. The description still adds real value beyond them: it declares the transform is pure and local (no dictionary lookup, no network) and that output is always ASCII. It does not address edge behavior such as exceeding the 4000-char maxLength or non-Livonian input, which keeps it from a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with the transformation, then the consumer, then the execution profile. Every sentence carries distinct information and nothing is redundant with the schema or annotations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an outputSchema present and 100% parameter coverage, the description doesn't need to explain return values, and it correctly confines itself to purpose, audience, and the pure-local-transform contract. It is nearly complete; only failure/edge behavior (length overflow, non-Livonian characters) is left unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the `text` parameter doc already carries the full mapping table, so the schema does the heavy lifting. The description only cross-references that doc ('See the `text` parameter doc for the exact letter mappings') rather than adding new parameter semantics, which is the textbook baseline-3 case.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb+resource: 'Strip Livonian orthography down to clean, pronounceable ASCII.' Combined with the direction of transformation (Livonian → ASCII), an agent can immediately separate this from livonian_deromanize, the inverse sibling, without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It names the intended consumers ('an English-trained downstream (a voice/TTS, a search box)'), which implies when to reach for it. However, it never explicitly states when NOT to use it or names livonian_deromanize as the alternative for the reverse direction, so the routing guidance is only implied.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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